• Journal of Sensor Science and Technology
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• v.30 no.1
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• pp.1-9
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• 2021

This mini review summarizes some of the recent advances in machine-learning (ML)-driven chemical and biological sensors. Specific focus is on field-effect-transistor (FET)-based sensors with a description of their structures and detection mechanisms. Key ML techniques are briefly reviewed for an audience not familiar with the basic principles. We mainly discuss two aspects: (1) data analysis based on ML and (2) ML applied to sensor design. In conclusion, the challenges and opportunities for the advancement of ML-based sensors are briefly considered.

• The KIPS Transactions:PartC
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• v.13C no.5 s.108
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• pp.541-548
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• 2006

Sensor network is a network for realizing the ubiquitous computing circumstances, which aggregates data by means of observation or detection deployed at the inaccessible places with the capacities of sensing and communication. To realize this circumstance, data which sensor nodes gathered from sensor networks are delivered to users, in which it is required to encrypt the data for the guarantee of secure communications. Therefore, it is needed to design key management scheme for encoding appropriate to the sensor nodes which feature continual data transfer, limited capacity of computation and storage and battery usage. We propose a key management scheme which is appropriate to sensor networks organizing hierarchical architecture. Because sensor nodes send data to their parent node, we can reduce routing energy. We assume that sensor nodes have different security levels by their levels in hierarchy. Our key management scheme provides different key establishment protocols according to the security levels of the sensor nodes. We reduce the number of sensor nodes which share the same key for encryption so that we reduce the damage by key exposure. Also, we propose key update protocols which take different terms for each level to update established keys efficiently for secure data encoding.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.8
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• pp.3629-3633
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• 2014

Background: Glioblastoma (GBM) is an immunosuppressive tumor whose median survival time is only 12-15 months, and patients with GBM have a uniformly poor prognosis. It is known that heredity contributes to formation of glioma, but there are few genetic studies concerning GBM. Materials and Methods: We genotyped six tagging SNPs (tSNP) in Han Chinese GBM and control patients. We used Microsoft Excel and SPSS 16.0 statistical package for statistical analysis and SNP Stats to test for associations between certain tSNPs and risk of GBM in five different models. ORs and 95%CIs were calculated for unconditional logistic-regression analysis with adjustment for age and gender. The SHEsis software platform was applied for analysis of linkage disequilibrium, haplotype construction, and genetic associations at polymorphism loci. Results: We found rs891835 in CCDC26 to be associated with GBM susceptibility at a level of p=0.009. The following genotypes of rs891835 were found to be associated with GBM risk in four different models of gene action: i) genotype GT (OR=2.26; 95%CI, 1.29-3.97; p=0.019) or GG (OR=1.33; 95%CI, 0.23-7.81; p=0.019) in the codominant model; ii) genotypes GT and GG (OR=2.18; 95%CI, 1.26-3.78; p=0.0061) in the dominant model; iii) GT (OR=2.24; 95%CI, 1.28-3.92; p=0.0053) in the overdominant model; iv) the allele G of rs891835 (OR=1.85; 95%CI, 1.14-3.00; p=0.015) in the additive model. In addition, "CG" and "CGGAG" were found by haplotype analysis to be associated with increased GBM risk. In contrast, genotype GG of CCDC26 rs6470745 was associated with decreased GBM risk (OR=0.34; 95%CI, 0.12-1.01; p=0.029) in the recessive model. Conclusions: Our results, combined with those from previous studies, suggest a potential genetic contribution of CCDC26 to GBM progression among Han Chinese.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.127-133
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• 2021

In industrial IoT environments, sensors generate data for their detection targets and deliver the data to IoT gateways. Therefore, managing large amounts of real-time sensor data is an essential feature for IoT gateways, and key-value storage engines are widely used to manage these sensor data. However, key-value storage engines used in IoT gateways do not take into account the characteristics of sensor data generated in industrial IoT environments, and this limits the performance of key-value storage engines. In this paper, we optimize the key-value storage engine by utilizing the features of sensor data in industrial IoT environments. The proposed optimization technique is to analyze the key, which is the input of a key-value storage engine, for further indexing. This reduces excessive write amplification and improves performance. We implement our optimization scheme in LevelDB and use the workload of the TPCx-IoT benchmark to evaluate our proposed scheme. From experimental results we show that our proposed technique achieves up to 21 times better than the existing scheme, and this shows that the proposed technique can perform high-speed data ingestion in industrial IoT environments.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.1
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• pp.41-55
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• 2007

The certificate is used to confirm and prove the user's identity in online finance and stocks business. A user's public key is stored in the certificate(for e.g., SignCert.der) and the private key, corresponding to public key, is stored in the private key file(for e.g., SignPri.key) after encryption using the password that he/she created for security. In this paper, we show that the certificate, deleted by the commercial certificate software, can be recovered without limitation using the commercial forensic tools. In addition, we explain the problem that the private key encryption password can be detected using the SignCert.der and the SignPri.key in off-line and propose the countermeasure about the problem.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.837-844
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• 2018

Recently, the cloud technology has made dynamical network changes by enabling the construction of a logical network without building a physical network. Despite recent research on the cloud, it is necessary to study security functions for the identification of fake virtual network functions and the encryption of communication between entities. Because the VNFs are open to subscribers and able to implement service directly, which can make them an attack target. In this paper, we propose a virtual public key infrastructure mechanism that detects a fake VNFs and guarantees data security through mutual authentication between VNFs. To evaluate the virtual PKI, we built a management and orchestration environment to test the performance of authentication and key generation for data security. And we test the detection of a distributed denial of service by using several AI algorithms to enhance the security in NFV.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.4 s.119
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• pp.379-388
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• 2007

This paper presents a real-time respiration and heartbeat detector comprised of a 1.6 GHz single-channel Doppler sensor and analog/digital signal processing block for remote vital sign detection. The RF front end of the Doppler sensor consists of an oscillator, mixer, low noise amplifier, branch-line hybrid and patch antenna. We apply artificial transmission lines(ATLs) to the branch-line hybrid, which leads to a size reduction of 40 % in the hybrid, while its performance is very comparable to that of a conventional hybrid. The analog signal conditioning block is implemented using second order Sallen-Key active filters and the digital signal processing block is realized with a LabVIEW program on a computer. The respiration and heartbeat detection is demonstrated at a distance of 50 cm using the developed system.

• Journal of Microbiology and Biotechnology
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• v.27 no.3
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• pp.624-632
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• 2017

Penicillium expansum causes blue mold rot, a prevalent postharvest disease of pome fruit, and is also the main producer of the patulin. However, knowledge on the molecular mechanisms involved in this pathogen-host interaction remains largely unknown. In this work, a two-dimensional gel electrophoresis-based proteomic approach was applied to probe changes in P. expansum 3.3703 cultivated in apple juice medium, which was used to mimic the in planta condition. The results showed that the pH value and reducing sugar content in the apple juice medium decreased whereas the patulin content increased with the growing of P. expansum. A total of 28 protein spots that were up-regulated in P. expansum when grown in apple juice medium were identified. Functional categorization revealed that the identified proteins were mainly related to carbohydrate metabolism, secondary metabolism, protein biosynthesis or degradation, and redox homeostasis. Remarkably, several induced proteins, including glucose dehydrogenase, galactose oxidase, and FAD-binding monooxygenase, which might be responsible for the observed medium acidification and patulin production, were also detected. Overall, the experimental results provide a comprehensive interpretation of the physiological and proteomic responses of P. expansum to the host plant environment, and future functional characterization of the identified proteins will deepen our understanding of fungi-host interactions.

• Current Optics and Photonics
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• v.2 no.1
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• pp.15-22
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• 2018

To measure atmospheric temperature, water vapor, and aerosol simultaneously, an efficient multi-function Raman lidar using an ultraviolet-wavelength laser has been developed. A high-performance spectroscopic box that utilizes multicavity interference filters, mounted sequentially at small angles of incidence, is used to separate the lidar return signals at different wavelengths, and to extract the signals with high efficiency. The external experiments are carried out for simultaneous detection of atmospheric temperature, water vapor, and aerosol extinction coefficient in Beijing, under clear and hazy weather conditions. The vertical profiles of temperature, water vapor, and aerosol extinction coefficient are analyzed. The results show that for an integration time of 5 min and laser energy of 200 mJ, the mean deviation between measurements obtained by lidar and radiosonde is small, and the overall trend is similar. The statistical temperature error for nighttime is below 1 K up to a height of 6.2 km under clear weather conditions, and up to a height of 2.5 km under slightly hazy weather conditions, with 5 min of observation time. An effective range for simultaneous detection of temperature and water vapor of up to 10 km is achieved. The temperature-inversion layer is found in the low troposphere. Continuous observations verify the reliability of Raman lidar to achieve real-time measurement of atmospheric parameters in the troposphere.

• Journal of Convergence for Information Technology
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• v.10 no.6
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• pp.19-27
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• 2020

In this study, Developing high resolution camera and Social Network Service sharing image can be easily getting images, it cause about taking fingerprints to easy from images. So I present solution about prevent to taking fingerprints. this technology is develop python using to opencv, blur libraries. First of all 'Hand Key point Detection' algorithm is used to locate the hand in the image. Using this algorithm can be find finger joints that can be protected while minimizing damage in the original image by using the coordinates of separate blurring the area of fingerprints in the image. from now on the development of accurate finger tracking algorithms, fingerprints will be protected by using technology as an internal option for smartphone camera apps from high resolution images.